Effects of diversity on diversity: consequences of competition and facilitation
Bibliographic record
Abstract
Diversity in one group of species or genotypes is often correlated with diversity in a second group – prominent examples including native vs exotic species, and genetic diversity in a focal species vs species diversity in the rest of the community. I used simulation models to investigate the roles of competition and facilitation among species or genotypes in creating diversity–diversity relationships, with a focus on facilitation, which has received little theoretical attention. When competitive interactions dominate, increasing diversity in one group reduces diversity in the second group via filling of available niche space. Facilitation can create positive diversity–diversity relationships via a sampling effect, whereby a strong facilitator of the second group is more likely to be present as diversity increases in the first group, and also via one group acting as a source of biotic heterogeneity (i.e. diversifying selection) on the second group. However, the biotic heterogeneity effect is expected only under restricted conditions – with asymmetric facilitation, only during a transient period, or only over a small range of species diversity levels – and therefore seems unlikely to operate within trophic levels in natural communities. More generally, the simultaneous operation of competition and facilitation results in several different diversity–diversity relationships and underlying mechanisms. The results clarify the potential roles of positive and negative interactions in creating diversity–diversity relationships, and in determining the outcome of community dynamics in general. This study also highlights some important difficulties in incorporating facilitation into ecological theory for communities with many species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".